





"""simple docstring"""





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A											=						logging.get_logger(__name__)

__A											=						{"""vocab_file""": """sentencepiece.bpe.model"""}

__A											=						{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

__A											=						{
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

__A											=						"""▁"""






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:str						    =   VOCAB_FILES_NAMES
       __magic_name__	:Optional[Any]						    =   PRETRAINED_VOCAB_FILES_MAP
       __magic_name__	:Optional[Any]						    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __magic_name__	:Tuple						    =   ["""input_ids""", """attention_mask"""]






       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase="<s>"       ,					__UpperCAmelCase="</s>"       ,					__UpperCAmelCase="</s>"       ,					__UpperCAmelCase="<s>"       ,					__UpperCAmelCase="<unk>"       ,					__UpperCAmelCase="<pad>"       ,					__UpperCAmelCase="<mask>"       ,					__UpperCAmelCase = None       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else mask_token

         lowerCAmelCase__     :Optional[Any]											=				{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=__UpperCAmelCase       ,					eos_token=__UpperCAmelCase       ,					unk_token=__UpperCAmelCase       ,					sep_token=__UpperCAmelCase       ,					cls_token=__UpperCAmelCase       ,					pad_token=__UpperCAmelCase       ,					mask_token=__UpperCAmelCase       ,					sp_model_kwargs=self.sp_model_kwargs       ,					**__UpperCAmelCase       ,					)

         lowerCAmelCase__     :Dict											=				vocab_file
         lowerCAmelCase__     :Tuple											=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(str(__UpperCAmelCase ) )

         lowerCAmelCase__     :Optional[int]											=				{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

         lowerCAmelCase__     :Tuple											=				len(self.sp_model ) - 1
         lowerCAmelCase__     :Any											=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         lowerCAmelCase__     :int											=				[self.cls_token_id]
         lowerCAmelCase__     :Optional[Any]											=				[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = False ):



         '''simple docstring'''

         if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__UpperCAmelCase       ,					token_ids_a=__UpperCAmelCase       ,					already_has_special_tokens=__UpperCAmelCase )

         if token_ids_a is None:
           return [1] + ([0] * len(__UpperCAmelCase )) + [1]
         return [1] + ([0] * len(__UpperCAmelCase )) + [1, 1] + ([0] * len(__UpperCAmelCase )) + [1]






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				[self.sep_token_id]
         lowerCAmelCase__     :Union[str, Any]											=				[self.cls_token_id]

         if token_ids_a is None:
           return len(cls + token_ids_a + sep ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return len(self.sp_model )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				{self.convert_ids_to_tokens(__UpperCAmelCase ): i for i in range(self.vocab_size )}
         vocab.update(self.added_tokens_encoder )
         return vocab






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         return self.sp_model.encode(__UpperCAmelCase       ,					out_type=__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
         lowerCAmelCase__     :Any											=				self.sp_model.PieceToId(__UpperCAmelCase )

         return spm_id if spm_id else self.unk_token_id






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				[]
         lowerCAmelCase__     :str											=				''
         lowerCAmelCase__     :Dict											=				False
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             if not prev_is_special:
               out_string += " "
             out_string += self.sp_model.decode(__UpperCAmelCase ) + token
             lowerCAmelCase__     :List[str]											=				True
             lowerCAmelCase__     :List[Any]											=				[]
           else:
             current_sub_tokens.append(__UpperCAmelCase )
             lowerCAmelCase__     :Union[str, Any]											=				False
         out_string += self.sp_model.decode(__UpperCAmelCase )
         return out_string.strip()






       def __getstate__(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				self.__dict__.copy()
         lowerCAmelCase__     :List[str]											=				None
         return state






       def __setstate__(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				d

         # for backward compatibility
         if not hasattr(self       ,					'sp_model_kwargs' ):
           lowerCAmelCase__     :List[Any]											=				{}

         lowerCAmelCase__     :Optional[Any]											=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(self.vocab_file )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         if not os.path.isdir(__UpperCAmelCase ):
           logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
           return
         lowerCAmelCase__     :Tuple											=				os.path.join(
             __UpperCAmelCase       ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

         if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
           copyfile(self.vocab_file       ,					__UpperCAmelCase )
         elif not os.path.isfile(self.vocab_file ):
           with open(__UpperCAmelCase       ,					'wb' ) as fi:
             lowerCAmelCase__     :List[Any]											=				self.sp_model.serialized_model_proto()
             fi.write(__UpperCAmelCase )

         return (out_vocab_file,)








"""simple docstring"""





from scipy.stats import pearsonr

import datasets


_SCREAMING_SNAKE_CASE   :							List[str]          =		'''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''


_SCREAMING_SNAKE_CASE   :							int          =		'''
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        [\'p-value\', \'pearsonr\']
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74
        >>> print(round(results[\'p-value\'], 2))
        0.15
'''


_SCREAMING_SNAKE_CASE   :							Dict          =		'''
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    , _KWARGS_DESCRIPTION						)
class 	a		(   datasets.Metric						):






    def   UpperCamelCase			(			self	:				Dict   )	->						Any:
      return datasets.MetricInfo(
          description=_DESCRIPTION			,		citation=_CITATION			,		inputs_description=_KWARGS_DESCRIPTION			,		features=datasets.Features(
              {
                  'predictions': datasets.Value('float'   ),
                  'references': datasets.Value('float'   ),
              }   )			,		reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html']			,		)






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				Tuple=False   )	->						Tuple:
      if return_pvalue:
        lowerCamelCase_       	=		pearsonr(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
        return {"pearsonr": results[0], "p-value": results[1]}
      else:
        return {"pearsonr": float(pearsonr(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )[0]   )}




